CAMELS: Catchment Attributes for Large-Sample Studies

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This dataset covers the same 671 catchments as the Large-Sample Hydrometeorological Dataset introduced by Newman et al. (2015). For each catchment, we characterized a wide range of attributes that influence catchment behavior and hydrological processes. Datasets characterizing these attributes have been available separately for some time, but comprehensive multivariate catchment scale assessments have so far been difficult, because these datasets typically have different spatial configurations, are stored in different archives, or use different data formats. By creating catchment scale estimates of these attributes, our aim is to simplify the assessment of their interrelationships.

Topographic characteristics (e.g. elevation and slope) were retrieved from Newman et al. (2015). Climatic indices (e.g., aridity and frequency of dry days) and hydrological signatures (e.g., mean annual discharge and baseflow index) were computed using the time series provided by Newman et al. (2015). Soil characteristics (e.g., porosity and soil depth) were characterized using the STATSGO data set and the Pelletier et al. (2016) data set. Vegetation characteristics (e.g. the leaf area index and the rooting depth) were inferred using MODIS data. Geological characteristics (e.g., geologic class and the subsurface porosity) were computed using the GLiM and GLHYMPS data sets.

An essential feature, that differentiates this data set from similar ones, is that it both provides quantitative estimates of diverse catchment attributes, and involves assessments of the limitations of the data and methods used to compute those attributes (see Addor et al., 2017). The large number of catchments, combined with the diversity of their geophysical characteristics, makes this new data well suited for large-sample studies and comparative hydrology.

The hydrometeorological time series provided by Newman et al. (2015) together with the catchment attributes described here constitute the CAMELS data set: Catchment Attributes and MEteorology for Large-sample Studies.

Figure. Four examples of attributes characterized over the contiguous United States.

Dataset Details

Release date:   2017,   last updated October 2017

Versions:   2.0,  available

Access information:   freely available at

How to cite this resource:

    Addor, N., A.J. Newman, N. Mizukami, and M.P. Clark, 2017: The CAMELS data set: catchment attributes and meteorology for large-sample studies. version 2.0. Boulder, CO: UCAR/NCAR. doi:10.5065/D6G73C3Q

Dataset Attributes

Format/size:   ascii files, 1 MB

Coverage:   671 watersheds across contiguous US

Dataset details:   Addor et al., HESS, 2017


Development Team

NCAR: Martyn Clark (PI), Nans Addor, Andrew Newman, Naoki Mizukami

Contact: Nans Addor |



U.S. Army Corps of Engineers


More Information

Related project information:   meteorological_datasets

Related datasets:   CAMELS_timeseries

Related Papers:

    Addor, N., A.J. Newman, N. Mizukami, and M.P. Clark, 2017: The CAMELS data set: catchment attributes and meteorology for large-sample studies. Hydrol. and Earth Syst. Sci., 21, 5293-5313, doi:10.5194/hess-21-5293-2017
    Pelletier, J.D., P.D. Broxton, P. Hazenberg, X. Zeng, P.A. Troch, G.-Y. Niu, Z. Williams, M.A. Brunke, and D. Gochis, 2016: A gridded global data set of soil, intact regolith, and sedimentary deposit thicknesses for regional and global land surface modeling. J. Adv. Model. Earth Syst., doi:10.1002/2015MS000526
    Newman, A. J., M.P. Clark, K. Sampson, A.W. Wood, L.E. Hay, A. Bock, R.J. Viger, D. Blodgett, L. Brekke, J.R. Arnold, T. Hopson, and Q. Duan, 2015: Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: data set characteristics and assessment of regional variability in hydrologic model performance. Hydrology and Earth System Sciences, 19, 209-223, doi:10.5194/hess-19-209-2015


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